Differentially private deep learning for load forecasting on smart grid

Ustundag Soykan, Elif, Bilgin, Zeki, Ersoy, Mehmet Akif and Tomur, Emrah (2019) Differentially private deep learning for load forecasting on smart grid. In: 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings. 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings . The Institute of Electrical and Electronics Engineers (IEEE), USA. ISBN 9781728109602

Full text not available from this repository. (Request a copy)


Load forecasting is vital for a reliable and sustainable smart grid as it is used to predict the demand and make price adjustment accordingly. Electric consumption data which is gathered from IoT devices like smart meter or smart appliances is a key input to improve the accuracy of the forecasting task. However, this data can leak private information of the householders as the consumption data reflects the behavioral patterns of the individuals. Providing privacy for the data without compromising the utility of the forecast is a challenging problem and this is where the differential privacy comes in to play. In this work, we present a practical implementation of the privacy preserving load forecasting with differential privacy techniques using Tensorflow Privacy library. We show that privacy guarantee for the data can be achieved to varying degrees with a tolerable degradation in the forecast results. We provide privacy-utility tradeoff values in our experiments for different privacy levels.

Item Type: Book Section
Additional Information: Funding Information: ACKNOWLEDGMENT This work was funded by The Scientific and Technological Research Council of Turkey, under 1515 Frontier RD Laboratories Support Program with project no:5169902. Publisher Copyright: © 2019 IEEE.
Uncontrolled Keywords: deep learning,differential privacy,iot privacy,load forecasting,lstm,smart grid,tensorflow,computer networks and communications,hardware and architecture,software,control and optimization,sdg 7 - affordable and clean energy ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 18 Aug 2022 12:30
Last Modified: 06 Jan 2023 11:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/87430
DOI: 10.1109/GCWkshps45667.2019.9024520

Actions (login required)

View Item View Item